Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations683
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)1.2%
Total size in memory64.0 KiB
Average record size in memory96.0 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 8 (1.2%) duplicate rowsDuplicates
Bare_Nuclei is highly overall correlated with Bland_Chromatin and 7 other fieldsHigh correlation
Bland_Chromatin is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
Class is highly overall correlated with Bare_Nuclei and 8 other fieldsHigh correlation
Clump_Thickness is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
Marginal_Adhesion is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
Mitoses is highly overall correlated with Class and 2 other fieldsHigh correlation
Normal_Nucleoli is highly overall correlated with Bare_Nuclei and 8 other fieldsHigh correlation
Single_cell_Size is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
U_Cell_Shape is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
U_Cell_Size is highly overall correlated with Bare_Nuclei and 8 other fieldsHigh correlation

Reproduction

Analysis started2024-12-21 11:24:41.806600
Analysis finished2024-12-21 11:24:56.024601
Duration14.22 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Sample_No
Real number (ℝ)

Distinct630
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1076720.2
Minimum63375
Maximum13454352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:56.159953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum63375
5-th percentile413929.8
Q1877617
median1171795
Q31238705
95-th percentile1334001.2
Maximum13454352
Range13390977
Interquartile range (IQR)361088

Descriptive statistics

Standard deviation620644.05
Coefficient of variation (CV)0.57642091
Kurtosis257.36841
Mean1076720.2
Median Absolute Deviation (MAD)104296
Skewness13.74841
Sum7.3539992 × 108
Variance3.8519903 × 1011
MonotonicityNot monotonic
2024-12-21T11:24:56.475528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1182404 6
 
0.9%
1276091 5
 
0.7%
1198641 3
 
0.4%
897471 2
 
0.3%
1168736 2
 
0.3%
411453 2
 
0.3%
734111 2
 
0.3%
1293439 2
 
0.3%
560680 2
 
0.3%
1143978 2
 
0.3%
Other values (620) 655
95.9%
ValueCountFrequency (%)
63375 1
0.1%
76389 1
0.1%
95719 1
0.1%
128059 1
0.1%
142932 1
0.1%
144888 1
0.1%
145447 1
0.1%
160296 1
0.1%
167528 1
0.1%
183913 1
0.1%
ValueCountFrequency (%)
13454352 1
0.1%
8233704 1
0.1%
1371920 1
0.1%
1371026 1
0.1%
1369821 1
0.1%
1368882 1
0.1%
1368273 1
0.1%
1368267 1
0.1%
1365328 1
0.1%
1365075 1
0.1%

Clump_Thickness
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4421669
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:56.660740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8207613
Coefficient of variation (CV)0.6349967
Kurtosis-0.63312453
Mean4.4421669
Median Absolute Deviation (MAD)2
Skewness0.58765424
Sum3034
Variance7.9566944
MonotonicityNot monotonic
2024-12-21T11:24:56.805870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 139
20.4%
5 128
18.7%
3 104
15.2%
4 79
11.6%
10 69
10.1%
2 50
 
7.3%
8 44
 
6.4%
6 33
 
4.8%
7 23
 
3.4%
9 14
 
2.0%
ValueCountFrequency (%)
1 139
20.4%
2 50
 
7.3%
3 104
15.2%
4 79
11.6%
5 128
18.7%
6 33
 
4.8%
7 23
 
3.4%
8 44
 
6.4%
9 14
 
2.0%
10 69
10.1%
ValueCountFrequency (%)
10 69
10.1%
9 14
 
2.0%
8 44
 
6.4%
7 23
 
3.4%
6 33
 
4.8%
5 128
18.7%
4 79
11.6%
3 104
15.2%
2 50
 
7.3%
1 139
20.4%

U_Cell_Size
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1508053
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:56.939093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0651449
Coefficient of variation (CV)0.97281317
Kurtosis0.07367914
Mean3.1508053
Median Absolute Deviation (MAD)0
Skewness1.2264041
Sum2152
Variance9.395113
MonotonicityNot monotonic
2024-12-21T11:24:57.106019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 373
54.6%
10 67
 
9.8%
3 52
 
7.6%
2 45
 
6.6%
4 38
 
5.6%
5 30
 
4.4%
8 28
 
4.1%
6 25
 
3.7%
7 19
 
2.8%
9 6
 
0.9%
ValueCountFrequency (%)
1 373
54.6%
2 45
 
6.6%
3 52
 
7.6%
4 38
 
5.6%
5 30
 
4.4%
6 25
 
3.7%
7 19
 
2.8%
8 28
 
4.1%
9 6
 
0.9%
10 67
 
9.8%
ValueCountFrequency (%)
10 67
 
9.8%
9 6
 
0.9%
8 28
 
4.1%
7 19
 
2.8%
6 25
 
3.7%
5 30
 
4.4%
4 38
 
5.6%
3 52
 
7.6%
2 45
 
6.6%
1 373
54.6%

U_Cell_Shape
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2152269
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:57.239442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9885808
Coefficient of variation (CV)0.92950851
Kurtosis-0.016815621
Mean3.2152269
Median Absolute Deviation (MAD)0
Skewness1.15789
Sum2196
Variance8.9316153
MonotonicityNot monotonic
2024-12-21T11:24:57.392027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 346
50.7%
10 58
 
8.5%
2 58
 
8.5%
3 53
 
7.8%
4 43
 
6.3%
5 32
 
4.7%
7 30
 
4.4%
6 29
 
4.2%
8 27
 
4.0%
9 7
 
1.0%
ValueCountFrequency (%)
1 346
50.7%
2 58
 
8.5%
3 53
 
7.8%
4 43
 
6.3%
5 32
 
4.7%
6 29
 
4.2%
7 30
 
4.4%
8 27
 
4.0%
9 7
 
1.0%
10 58
 
8.5%
ValueCountFrequency (%)
10 58
 
8.5%
9 7
 
1.0%
8 27
 
4.0%
7 30
 
4.4%
6 29
 
4.2%
5 32
 
4.7%
4 43
 
6.3%
3 53
 
7.8%
2 58
 
8.5%
1 346
50.7%

Marginal_Adhesion
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8301611
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:57.523643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8645622
Coefficient of variation (CV)1.0121552
Kurtosis0.94240721
Mean2.8301611
Median Absolute Deviation (MAD)0
Skewness1.5091811
Sum1933
Variance8.2057165
MonotonicityNot monotonic
2024-12-21T11:24:57.655816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 393
57.5%
3 58
 
8.5%
2 58
 
8.5%
10 55
 
8.1%
4 33
 
4.8%
8 25
 
3.7%
5 23
 
3.4%
6 21
 
3.1%
7 13
 
1.9%
9 4
 
0.6%
ValueCountFrequency (%)
1 393
57.5%
2 58
 
8.5%
3 58
 
8.5%
4 33
 
4.8%
5 23
 
3.4%
6 21
 
3.1%
7 13
 
1.9%
8 25
 
3.7%
9 4
 
0.6%
10 55
 
8.1%
ValueCountFrequency (%)
10 55
 
8.1%
9 4
 
0.6%
8 25
 
3.7%
7 13
 
1.9%
6 21
 
3.1%
5 23
 
3.4%
4 33
 
4.8%
3 58
 
8.5%
2 58
 
8.5%
1 393
57.5%

Single_cell_Size
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2342606
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:57.775621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2230855
Coefficient of variation (CV)0.68735508
Kurtosis2.1296393
Mean3.2342606
Median Absolute Deviation (MAD)0
Skewness1.7037164
Sum2209
Variance4.9421089
MonotonicityNot monotonic
2024-12-21T11:24:57.922728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 376
55.1%
3 71
 
10.4%
4 48
 
7.0%
1 44
 
6.4%
6 40
 
5.9%
5 39
 
5.7%
10 31
 
4.5%
8 21
 
3.1%
7 11
 
1.6%
9 2
 
0.3%
ValueCountFrequency (%)
1 44
 
6.4%
2 376
55.1%
3 71
 
10.4%
4 48
 
7.0%
5 39
 
5.7%
6 40
 
5.9%
7 11
 
1.6%
8 21
 
3.1%
9 2
 
0.3%
10 31
 
4.5%
ValueCountFrequency (%)
10 31
 
4.5%
9 2
 
0.3%
8 21
 
3.1%
7 11
 
1.6%
6 40
 
5.9%
5 39
 
5.7%
4 48
 
7.0%
3 71
 
10.4%
2 376
55.1%
1 44
 
6.4%

Bare_Nuclei
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5446559
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:58.060863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6438572
Coefficient of variation (CV)1.0279861
Kurtosis-0.79884414
Mean3.5446559
Median Absolute Deviation (MAD)0
Skewness0.99001565
Sum2421
Variance13.277695
MonotonicityNot monotonic
2024-12-21T11:24:58.194636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 402
58.9%
10 132
 
19.3%
2 30
 
4.4%
5 30
 
4.4%
3 28
 
4.1%
8 21
 
3.1%
4 19
 
2.8%
9 9
 
1.3%
7 8
 
1.2%
6 4
 
0.6%
ValueCountFrequency (%)
1 402
58.9%
2 30
 
4.4%
3 28
 
4.1%
4 19
 
2.8%
5 30
 
4.4%
6 4
 
0.6%
7 8
 
1.2%
8 21
 
3.1%
9 9
 
1.3%
10 132
 
19.3%
ValueCountFrequency (%)
10 132
 
19.3%
9 9
 
1.3%
8 21
 
3.1%
7 8
 
1.2%
6 4
 
0.6%
5 30
 
4.4%
4 19
 
2.8%
3 28
 
4.1%
2 30
 
4.4%
1 402
58.9%

Bland_Chromatin
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4450952
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:58.323801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4496966
Coefficient of variation (CV)0.7110679
Kurtosis0.16764564
Mean3.4450952
Median Absolute Deviation (MAD)1
Skewness1.0952705
Sum2353
Variance6.0010133
MonotonicityNot monotonic
2024-12-21T11:24:58.446252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 161
23.6%
2 160
23.4%
1 150
22.0%
7 71
10.4%
4 39
 
5.7%
5 34
 
5.0%
8 28
 
4.1%
10 20
 
2.9%
9 11
 
1.6%
6 9
 
1.3%
ValueCountFrequency (%)
1 150
22.0%
2 160
23.4%
3 161
23.6%
4 39
 
5.7%
5 34
 
5.0%
6 9
 
1.3%
7 71
10.4%
8 28
 
4.1%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.1%
7 71
10.4%
6 9
 
1.3%
5 34
 
5.0%
4 39
 
5.7%
3 161
23.6%
2 160
23.4%
1 150
22.0%

Normal_Nucleoli
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8696925
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:58.590381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0526664
Coefficient of variation (CV)1.0637608
Kurtosis0.4735883
Mean2.8696925
Median Absolute Deviation (MAD)0
Skewness1.4204311
Sum1960
Variance9.3187722
MonotonicityNot monotonic
2024-12-21T11:24:58.724817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 432
63.3%
10 60
 
8.8%
3 42
 
6.1%
2 36
 
5.3%
8 23
 
3.4%
6 22
 
3.2%
5 19
 
2.8%
4 18
 
2.6%
7 16
 
2.3%
9 15
 
2.2%
ValueCountFrequency (%)
1 432
63.3%
2 36
 
5.3%
3 42
 
6.1%
4 18
 
2.6%
5 19
 
2.8%
6 22
 
3.2%
7 16
 
2.3%
8 23
 
3.4%
9 15
 
2.2%
10 60
 
8.8%
ValueCountFrequency (%)
10 60
 
8.8%
9 15
 
2.2%
8 23
 
3.4%
7 16
 
2.3%
6 22
 
3.2%
5 19
 
2.8%
4 18
 
2.6%
3 42
 
6.1%
2 36
 
5.3%
1 432
63.3%

Mitoses
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6032211
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-12-21T11:24:58.852624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7326741
Coefficient of variation (CV)1.0807456
Kurtosis12.273374
Mean1.6032211
Median Absolute Deviation (MAD)0
Skewness3.5114762
Sum1095
Variance3.0021597
MonotonicityNot monotonic
2024-12-21T11:24:58.992161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 563
82.4%
2 35
 
5.1%
3 33
 
4.8%
10 14
 
2.0%
4 12
 
1.8%
7 9
 
1.3%
8 8
 
1.2%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 563
82.4%
2 35
 
5.1%
3 33
 
4.8%
4 12
 
1.8%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.2%
10 14
 
2.0%
ValueCountFrequency (%)
10 14
 
2.0%
8 8
 
1.2%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.8%
3 33
 
4.8%
2 35
 
5.1%
1 563
82.4%

Class
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
0
444 
1
239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters683
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 444
65.0%
1 239
35.0%

Length

2024-12-21T11:24:59.166189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T11:24:59.305207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 444
65.0%
1 239
35.0%

Most occurring characters

ValueCountFrequency (%)
0 444
65.0%
1 239
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 444
65.0%
1 239
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 444
65.0%
1 239
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 444
65.0%
1 239
35.0%

Interactions

2024-12-21T11:24:54.322130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:42.197565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:43.660259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:44.946964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:46.299112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:47.582694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:48.919627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:50.451537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:51.773955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:53.035515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:54.464673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:42.391804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:43.813566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:45.104442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:46.453461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:47.732963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:49.114878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:50.586668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:51.904869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:53.195879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:54.571969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:42.523139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:43.953992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:45.272102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:46.565552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:47.870241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:49.255539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:50.715200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:52.035916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:53.308465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:54.710156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:42.663352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:44.081394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:45.396304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:46.704404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:47.998517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:49.367416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:50.887036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:52.153639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:53.443640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:54.830475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:42.776299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:44.196967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:45.531373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:46.834915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:48.133336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:49.485672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:51.040065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:52.290966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:53.553569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:54.937909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:42.933655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:44.314900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:45.648086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:46.971561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:48.249755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:49.705961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:51.151876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:52.412656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:53.694610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:55.077397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:43.064168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:44.443743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:45.787644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:47.082497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:48.383279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:49.817673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:51.288520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:52.535095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:53.815420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:55.202570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:43.197309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:44.564203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:45.921494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:47.220171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:48.506076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:49.957373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:51.410994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:52.660159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:53.941258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:55.312232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:43.331074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:44.704190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:46.053345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:47.332638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:48.616809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:50.088210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:51.518841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:52.786799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:54.076554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:55.438205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:43.547722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:44.814199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:46.181774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:47.470776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:48.757355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:50.211609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:51.636631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:52.913464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T11:24:54.201642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-21T11:24:59.421073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Bare_NucleiBland_ChromatinClassClump_ThicknessMarginal_AdhesionMitosesNormal_NucleoliSample_NoSingle_cell_SizeU_Cell_ShapeU_Cell_Size
Bare_Nuclei1.0000.6790.8390.5910.6970.4740.660-0.1320.6950.7530.770
Bland_Chromatin0.6791.0000.8070.5340.6290.3910.662-0.0950.6450.6950.721
Class0.8390.8071.0000.7360.7470.5190.7730.0000.8020.8680.882
Clump_Thickness0.5910.5340.7361.0000.5440.4210.566-0.0030.5870.6670.664
Marginal_Adhesion0.6970.6290.7470.5441.0000.4470.636-0.0560.6650.7190.745
Mitoses0.4740.3910.5190.4210.4471.0000.510-0.0820.4830.4780.513
Normal_Nucleoli0.6600.6620.7730.5660.6360.5101.000-0.0660.7110.7240.753
Sample_No-0.132-0.0950.000-0.003-0.056-0.082-0.0661.000-0.092-0.059-0.041
Single_cell_Size0.6950.6450.8020.5870.6650.4830.711-0.0921.0000.7650.793
U_Cell_Shape0.7530.6950.8680.6670.7190.4780.724-0.0590.7651.0000.895
U_Cell_Size0.7700.7210.8820.6640.7450.5130.753-0.0410.7930.8951.000

Missing values

2024-12-21T11:24:55.606363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-21T11:24:55.877715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Sample_NoClump_ThicknessU_Cell_SizeU_Cell_ShapeMarginal_AdhesionSingle_cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
01000025511121.03110
110029455445710.03210
21015425311122.03110
31016277688134.03710
41017023411321.03110
51017122810108710.09711
610180991111210.03110
71018561212121.03110
81033078211121.01150
91033078421121.02110
Sample_NoClump_ThicknessU_Cell_SizeU_Cell_ShapeMarginal_AdhesionSingle_cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
689654546111121.01180
690654546111321.01110
69169509151010545.04411
692714039311121.01110
693763235311121.02120
694776715311132.01110
695841769211121.01110
69688882051010373.081021
697897471486434.010611
698897471488545.010411

Duplicate rows

Most frequently occurring

Sample_NoClump_ThicknessU_Cell_SizeU_Cell_ShapeMarginal_AdhesionSingle_cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass# duplicates
03206753352310.071112
1466906111121.011102
2704097111111.021102
31100524610102810.073312
41116116910101108.033112
51198641311121.031102
61218860111111.031102
71321942511121.031102